Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum

Abstract Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features and to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51 with cortical and subcortical variant subtypes). Ninety-one were used as the training set and 20 as a test set. The presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia and supranuclear gaze palsy were noted. Domains of akinesia, ocular motor impairment, postural instability and cognitive dysfunction as defined by the Movement Disorders Society criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose-positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity, suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so sub-cortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI- and FDG-PET-based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic Impairment Scale was stronger than with the Movement Disorders Society Diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set; however, prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data-driven approaches to clinical neuroimaging analyses.


Assessment of scanner effect on MRI:
In the training set 54 patients had 3T MRI on GE scanners, and rest on Siemens.In the test set 4 had MRI on GE scanners and 16 on Siemens.All test and training FDG-PET scans were done on a GE PET/CT scanner.There were 2 components that exhibited a scanner effect, both in the direction of larger loads for participants scanned on Siemens.
• Component 1 (p-adjusted = 0.000179 for t-test comparing GE vs Siemens) • Component 4 (p-adjusted = 0.0134 for t-test comparing GE vs Siemens) • However, such a difference does not necessarily imply a bias in the resultsthat requires that there be differences on clinical measures in those scanned on GE vs Siemens.For binary measures, the following measures had the most evidence for a different between groups: Component 4 was not selected for any of the three models (coefficient = 0) whereas Component 1 formed part of all three.However, the R-square for models predicting these measures were among the worst (Eyes) or average (PSPRS Total) in the training set.
This argues strongly against results being influenced by scanner effect.We do not think it is good practice to hand pick components as it goes against the aims of a data driven approach, where high dimensional data are decomposed and then regularized models are used to select relevant components.

Detailed description of individual MRI Components
Deeper reds indicated more positive weights at that voxel, with weights representing the probability of tissue being present at that voxel.For example, if a patient had high loads on component 1, they would be expected to have higher tissue probability in the red areas, and conversely lower loads would indicate low probability of tissue there, i.e., atrophy.

Component 1
Bilateral temporal, with lower weights in biparietal areas

Cerebellum
Component 9 Bilateral superior cortical regions, including lateral precentral gyrus

Detailed description of FDG Components
Deeper reds indicated more positive weights at that voxel, with weights representing the FDG uptake (SUVR) at that voxel.For example, if a patient had high loads on component 1, they would be expected to have higher SUVR values in the red areas, and conversely lower loads would indicate lower SUVR there, i.e., hypometabolism.

Component 1
Biparietal predominant, with additional weights across lateral frontal and temporal regions Component 2 Right greater than left lateral frontal, parietal, and temporal weights

Component 3 5
Widespread deep cortical and juxtacortical weightsComponent 4 Cerebellum and brainstem, with additional subcortical gray matter and deep cortical/juxtacortical weights Component Brainstem and subcortical gray matter, with additional right greater than left frontoparietal weights Component 6

Table 1
Neither component was part of the model for FTD-syndrome.For Limb Apraxia, component 1 was selected, but with a coefficient that was very low -0.048 of the max coefficients in that model (i.e., contributing a lot less than other components).For continuous MRI measures, the following measures had the most evidence for a different between groups: